Exploring Gender Differences in Cognitive Load and Stress Responses: A Machine Learning Approach using Physiological Data in Learning Environments

Exploring Gender Differences in Cognitive Load and Stress Responses: A Machine Learning Approach using Physiological Data in Learning Environments | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Exploring Gender Differences in Cognitive Load and Stress Responses: A Machine Learning Approach using Physiological Data in Learning Environments Muhammad Arkaan Izhraqi, Maliha Mian, Nadine Marcus, Gelareh Mohammadi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6888326/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The rapid integration of technology in contemporary education presents new challenges, such as technology-related anxiety and test-induced stress that can impair learning outcomes. This study examines how stress affects cognitive load during a learning task and explores gender differences in physiological responses. Sixty participants (30 females and 30 males) were randomly assigned to the control and stress conditions while engaging in a learning task. Wearable sensors were continuously monitoring physiological indicators, such as heart rate and skin conductance, to capture real-time stress and cognitive-based data. A comprehensive data processing pipeline, coupled with machine learning techniques and feature reduction using hierarchical clustering, was applied to classify stress states and levels of cognitive load. Our findings indicate that physiological measures, particularly those derived from skin conductance, effectively differentiate between stressed and non-stressed states. Notably, models tailored to female participants achieved classification accuracies of up to 90%, suggesting more consistent stress responses compared to their male counterparts, who required a broader range of features to reach similar performance. While distinguishing cognitive load levels proved more challenging, the insights gained pave the way for developing adaptive, real-time monitoring systems that could enhance stress management and optimise personalised learning strategies. physiological monitoring educational technology stress classification cognitive load machine learning in education gender differences Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6888326","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":471374645,"identity":"17635be6-9bd4-48c4-9183-298839cd397f","order_by":0,"name":"Muhammad Arkaan Izhraqi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBElEQVRIiWNgGAWjYFACNjDJwyABogxsgARj4wFStKSBtDQQpYUBooXhMJjEq4V/9rHEDz93MMjIz+59+OFNwXm7te2HgbbU2ETj0iJxLu2wZO8ZBh7GOceNJecY3E7ediYRqOVYWm4DLj1n2BskeNsYeJgl0hikeYBazA4AtTA2HMapRf4Me/PPv0AtbBJpzL95DM4lm51/iF+LwRm2Y9IgW3gk0tiAthywM7tBwBbDM2xp1rJtEjwSQC2WcwySE8xuAG1JwOMXuTNsxjffttnYy89IY77x5o+dvdn59IcPPtTY4PY+BEAiBRihDIlglQn4lSMBoBZ7ohWPglEwCkbBiAEAw0dY+QmnI7kAAAAASUVORK5CYII=","orcid":"","institution":"University of New South Wales","correspondingAuthor":true,"prefix":"","firstName":"Muhammad","middleName":"Arkaan","lastName":"Izhraqi","suffix":""},{"id":471374646,"identity":"7e03c559-6630-4725-bf1f-4201c4344fcf","order_by":1,"name":"Maliha Mian","email":"","orcid":"","institution":"University of New South Wales","correspondingAuthor":false,"prefix":"","firstName":"Maliha","middleName":"","lastName":"Mian","suffix":""},{"id":471374647,"identity":"58736ca3-14e3-43dc-8b70-d02034420442","order_by":2,"name":"Nadine Marcus","email":"","orcid":"","institution":"University of New South Wales","correspondingAuthor":false,"prefix":"","firstName":"Nadine","middleName":"","lastName":"Marcus","suffix":""},{"id":471374648,"identity":"18de4f89-98d8-4bd9-ba9e-8c0a0a23e935","order_by":3,"name":"Gelareh Mohammadi","email":"","orcid":"","institution":"University of New South Wales","correspondingAuthor":false,"prefix":"","firstName":"Gelareh","middleName":"","lastName":"Mohammadi","suffix":""}],"badges":[],"createdAt":"2025-06-13 12:38:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6888326/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6888326/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104958411,"identity":"1ebf5e3f-00a0-444f-baf2-5dfdbdb7a54e","added_by":"auto","created_at":"2026-03-19 08:28:08","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":856135,"visible":true,"origin":"","legend":"","description":"","filename":"ArkaanExploringGenderDifferences.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6888326/v1_covered_fdc94cd1-d1a1-4730-b3a2-b58d14ed4899.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Exploring Gender Differences in Cognitive Load and Stress Responses: A Machine Learning Approach using Physiological Data in Learning Environments","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"physiological monitoring, educational technology, stress classification, cognitive load, machine learning in education, gender differences","lastPublishedDoi":"10.21203/rs.3.rs-6888326/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6888326/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe rapid integration of technology in contemporary education presents new challenges, such as technology-related anxiety and test-induced stress that can impair learning outcomes. This study examines how stress affects cognitive load during a learning task and explores gender differences in physiological responses. Sixty participants (30 females and 30 males) were randomly assigned to the control and stress conditions while engaging in a learning task. Wearable sensors were continuously monitoring physiological indicators, such as heart rate and skin conductance, to capture real-time stress and cognitive-based data. A comprehensive data processing pipeline, coupled with machine learning techniques and feature reduction using hierarchical clustering, was applied to classify stress states and levels of cognitive load. Our findings indicate that physiological measures, particularly those derived from skin conductance, effectively differentiate between stressed and non-stressed states. 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